已知负荷数据是一个24×5的数组，第一列是时间列，写一组lstm负荷预测的matlab代码，

好的，以下是一个简单的LSTM负荷预测的Matlab代码示例。假设你已经准备好了负荷数据数组load_data，其中第一列是时间列，而其他列是负荷数据。

% 按时间排序
load_data = sortrows(load_data, 1);

% 生成训练集和测试集
train_ratio = 0.8;
train_size = floor(train_ratio * size(load_data, 1));
train_data = load_data(1:train_size, 2:end);
test_data = load_data(train_size+1:end, 2:end);

% 数据归一化
train_data_norm = normalize(train_data);
test_data_norm = normalize(test_data);

% 准备训练数据
X_train = [];
Y_train = [];
n_steps = 3; % 每个时间步长包含的数据点数
for i = n_steps:size(train_data_norm, 1)
    X_train = [X_train; train_data_norm(i-n_steps+1:i, :)];
    Y_train = [Y_train; train_data_norm(i, :)];
end

% 调整训练数据的形状
X_train = reshape(X_train, [size(X_train,1), n_steps, size(train_data,2)-1]);
Y_train = reshape(Y_train, [size(Y_train,1), size(train_data,2)-1]);

% 构建LSTM模型
input_size = size(train_data,2)-1;
output_size = size(train_data,2)-1;
num_hidden_units = 64;
layers = [ ...
    sequenceInputLayer(input_size)
    lstmLayer(num_hidden_units,'OutputMode','last')
    fullyConnectedLayer(output_size)
    regressionLayer];

% 训练模型
opts = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.01, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropFactor',0.1, ...
    'LearnRateDropPeriod',30, ...
    'Verbose',0, ...
    'Plots','training-progress');
trained_net = trainNetwork(X_train, Y_train, layers, opts);

% 准备测试数据
X_test = [];
Y_test = [];
for i = n_steps:size(test_data_norm, 1)
    X_test = [X_test; test_data_norm(i-n_steps+1:i, :)];
    Y_test = [Y_test; test_data_norm(i, :)];
end

% 调整测试数据的形状
X_test = reshape(X_test, [size(X_test,1), n_steps, size(test_data,2)-1]);
Y_test = reshape(Y_test, [size(Y_test,1), size(test_data,2)-1]);

% 进行预测
Y_pred = predict(trained_net, X_test);

% 反归一化预测结果
Y_pred = Y_pred .* max(train_data) + min(train_data);
Y_test = Y_test .* max(train_data) + min(train_data);

% 绘制预测结果
figure
plot(Y_test(:,1), 'b')
hold on
plot(Y_pred(:,1), 'r')
legend('真实值', '预测值')
title('负荷预测结果')
xlabel('时间步长')
ylabel('负荷值')

需要注意的是，这只是一个简单的示例代码，实际应用中需要根据数据特点和需求进行调整和优化。